Meta Llama 3 8B Instruct GGUF — Hardware Requirements & GPU Compatibility
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118.1K downloads
101 likesSpecifications
- Publisher
- MaziyarPanahi
- Family
- Llama 3
- Parameters
- 8B
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HuggingFace
How Much VRAM Does Meta Llama 3 8B Instruct GGUF Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| IQ2_XS | 2.40 | 2.6 GB | — | 2.40 GB | Importance-weighted 2-bit, extra small |
| IQ3_XS | 3.30 | 3.6 GB | — | 3.30 GB | Importance-weighted 3-bit, extra small |
| Q2_K | 3.40 | 3.7 GB | — | 3.40 GB | 2-bit quantization with K-quant improvements |
| Q3_K_S | 3.50 | 3.9 GB | — | 3.50 GB | 3-bit small quantization |
| Q3_K_M | 3.90 | 4.3 GB | — | 3.90 GB | 3-bit medium quantization |
| Q3_K_L | 4.10 | 4.5 GB | — | 4.10 GB | 3-bit large quantization |
| IQ4_XS | 4.30 | 4.7 GB | — | 4.30 GB | Importance-weighted 4-bit, compact |
| Q4_K_S | 4.50 | 5.0 GB | — | 4.50 GB | 4-bit small quantization |
| Q4_K_M | 4.80 | 5.3 GB | — | 4.80 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_S | 5.50 | 6.0 GB | — | 5.50 GB | 5-bit small quantization |
| Q5_K_M | 5.70 | 6.3 GB | — | 5.70 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 7.3 GB | — | 6.60 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 8.8 GB | — | 8.00 GB | 8-bit quantization, near-lossless |
Which GPUs Can Run Meta Llama 3 8B Instruct GGUF?
Q4_K_M · 5.3 GBMeta Llama 3 8B Instruct GGUF (Q4_K_M) requires 5.3 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 7+ GB is recommended. 35 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.
Runs great
— Plenty of headroomWhich Devices Can Run Meta Llama 3 8B Instruct GGUF?
Q4_K_M · 5.3 GB33 devices with unified memory can run Meta Llama 3 8B Instruct GGUF, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomRelated Models
Frequently Asked Questions
- How much VRAM does Meta Llama 3 8B Instruct GGUF need?
Meta Llama 3 8B Instruct GGUF requires 5.3 GB of VRAM at Q4_K_M, or 8.8 GB at Q8_0.
VRAM = Weights + KV Cache + Overhead
Weights = 8B × 4.8 bits ÷ 8 = 4.8 GB
KV Cache + Overhead ≈ 0.5 GB (at 2K context + ~0.3 GB framework)
VRAM usage by quantization
Q4_K_M5.3 GB- What's the best quantization for Meta Llama 3 8B Instruct GGUF?
For Meta Llama 3 8B Instruct GGUF, Q4_K_M (5.3 GB) offers the best balance of quality and VRAM usage. Q5_K_S (6.0 GB) provides better quality if you have the VRAM. The smallest option is IQ2_XS at 2.6 GB.
VRAM requirement by quantization
IQ2_XS2.6 GB~57%Q3_K_S3.9 GB~77%IQ4_XS4.7 GB~87%Q4_K_M ★5.3 GB~89%Q5_K_S6.0 GB~92%Q8_08.8 GB~99%★ Recommended — best balance of quality and VRAM usage.
- Can I run Meta Llama 3 8B Instruct GGUF on a Mac?
Meta Llama 3 8B Instruct GGUF requires at least 2.6 GB at IQ2_XS, which exceeds the unified memory of most consumer Macs. You would need a Mac Studio or Mac Pro with a high-memory configuration.
- Can I run Meta Llama 3 8B Instruct GGUF locally?
Yes — Meta Llama 3 8B Instruct GGUF can run locally on consumer hardware. At Q4_K_M quantization it needs 5.3 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Meta Llama 3 8B Instruct GGUF?
At Q4_K_M, Meta Llama 3 8B Instruct GGUF can reach ~552 tok/s on AMD Instinct MI300X. On NVIDIA GeForce RTX 4090: ~124 tok/s. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.
tok/s = (bandwidth GB/s ÷ model GB) × efficiency
Example: AMD Instinct MI300X → 5300 ÷ 5.3 × 0.55 = ~552 tok/s
Estimated speed at Q4_K_M (5.3 GB)
AMD Instinct MI300X~552 tok/sNVIDIA GeForce RTX 4090~124 tok/sNVIDIA H100 SXM~413 tok/sAMD Instinct MI250X~341 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Meta Llama 3 8B Instruct GGUF?
At Q4_K_M, the download is about 4.80 GB. The full-precision Q8_0 version is 8.00 GB. The smallest option (IQ2_XS) is 2.40 GB.